System and method for large-scale radio frequency signal collection and processing

ABSTRACT

A large-scale radio frequency signal collection and processing system comprising a plurality of sensor systems mounted on a plurality of collection platforms that integrates a plurality of overlapping datasets with differing characteristics (e.g., different resolutions, different view angles, different heights, different time periods, unrelated types of data) to generate an enriched dataset or datasets using a variety of processing techniques (e.g., statistical analysis, signal processing, image processing) that allows for more comprehensive analysis of the radio frequency signal landscape than would be possible using any of the datasets individually, or in combination but without such integration.

CROSS-REFERENCE TO RELATED APPLICATIONS

application No. Date Filed Title Current Herewith SYSTEM AND METHOD FORLARGE- application SCALE RADIO FREQUENCY SIGNAL COLLECTION ANDPROCESSING Is a continuation of: 16/822,239 Mar. 18, 2020 SYSTEM ANDMETHOD FOR LARGE- SCALE RADIO FREQUENCY SIGNAL COLLECTION AND PROCESSINGwhich is a continuation-in-part of: 16/808,327 Mar. 3, 2020 LARGE SCALERADIO FREQUENCY SIGNAL INFORMATION PROCESSING AND ANALYSIS SYSTEM USINGBIN-WISE PROCESSING which is a continuation of: 16/384,621 Apr. 15, 2019LARGE SCALE RADIO FREQUENCY U.S. Pat. No. Issue Date SIGNAL INFORMATIONPROCESSING 10,582,401 Mar. 3, 2020 AND ANALYSIS SYSTEM which is acontinuation-in-part of: 15/991,540 May 29, 2018 SYSTEM AND METHODS FORDETECTING AND CHARACTERIZING U.S. Pat. No. Issue Date ELECTROMAGNETICEMISSIONS 10,338,118 Jul. 2, 2019 which claims benefit of and priorityto: 62/656,781 Apr. 12, 2018 SYSTEM AND METHODS FOR DETECTING ANDCHARACTERIZING ELECTROMAGNETIC EMISSIONS Current Herewith SYSTEM ANDMETHOD FOR LARGE- application SCALE RADIO FREQUENCY SIGNAL COLLECTIONAND PROCESSING Is a continuation of: 16/822,239 Mar. 18, 2020 SYSTEM ANDMETHOD FOR LARGE- SCALE RADIO FREQUENCY SIGNAL COLLECTION AND PROCESSINGwhich is a continuation-in-part of: 16/808,327 Mar. 3, 2020 LARGE SCALERADIO FREQUENCY SIGNAL INFORMATION PROCESSING AND ANALYSIS SYSTEM USINGBIN-WISE PROCESSING which is a continuation of: 16/384,621 Apr. 15, 2019LARGE SCALE RADIO FREQUENCY U.S. Pat. No. Issue Date SIGNAL INFORMATIONPROCESSING 10,582,401 Mar. 3, 2020 AND ANALYSIS SYSTEM which is acontinuation-in-part of: 15/585,102 May 2, 2017 SYSTEMS AND METHODS FORU.S. Pat. No. Issue Date MEASURING TERRESTRIAL SPECTRUM 10,684,347 Jun.16, 2020 FROM SPACE which claims benefit of and priority to: 62/305,513Mar. 8, 2016 SYSTEMS AND METHODS FOR MEASURING TERRESTRIAL SPECTRUM FROMSPACE

the entire specification of each of which is incorporated herein byreference.

BACKGROUND Field of the Art

The disclosure relates to the field of radio frequency signal collectionand processing.

Discussion of the State of the Art

Most wireless telecommunication requires transmission and reception ofradio frequency (radio frequency signal) signals in the radio frequencysignal spectrum from frequencies of about 3 kHz to frequencies of about300 GHz. Mobile, backhaul, consumer, fixed station, and public safetycommunications, to name a few, rely on frequencies in the radiofrequency signal spectrum that are assigned, or allocated, for aparticular use. Further, certain frequency bands are licensed toparticular users in specified geographical areas.

Due to the proliferation of wireless communication applications,frequency bands for wireless communication in the radio frequency signalspectrum are becoming congested. Efforts continue to be made to increasethe efficiency of frequency usage by consolidating and assigning unusedor minimally used frequencies, and by reallocating frequencies for useas demand dictates. Additionally, transmitter output power is beingreduced to limit the effective area of a transmitted signal so thatfrequencies can be reused based on geographical diversity. Consumerdemand and trends towards high-data rate communications, which requiresthe use of increasing bandwidth and efficient transmissions methods,drive these innovations. As the complexity of wireless systems naturallyincreases to support more users in discrete bands, better systematicknowledge of the spectrum environment is needed to ensure properoperation. Indeed, proposed approaches such as dynamic spectrumallocation and cognitive radio techniques for spectrum sharing, tofurther enhance efficiency of spectrum use, require good knowledge ofthe spectral environment.

What is needed is a system and method for implementing large-scale radiofrequency signal collection, processing, analysis, and mapping thatallows for a comprehensive understanding of the radio frequency signallandscape over a wide geographical area, across a broad spectrum ofradio frequency signals, and across a variety of time periods.

SUMMARY

Accordingly, the inventor has conceived and reduced to practice, alarge-scale radio frequency signal collection and processing systemcomprising one or more sensor systems mounted on one or more collectionplatforms that integrates a plurality of overlapping datasets withdiffering characteristics (e.g., different resolutions, different viewangles, different heights, different time periods, unrelated types ofdata) to generate an enriched dataset or datasets using a variety ofprocessing techniques (e.g., statistical analysis, signal processing,image processing) that allow for more comprehensive analysis of theradio frequency signal landscape than would be possible using any of thedatasets individually, or in combination but without such integration.

According to a preferred embodiment, a system for large-area,wide-bandwidth radio frequency signal collection and processing isdisclosed, comprising: one or more collection platforms, eachcomprising: one or more radio frequency sensor systems configured tocapture signal information comprising radio frequency data; and one ormore metadata collectors configured to capture signal informationcomprising metadata about the radio frequency data, including at least atime, a frequency, and a location of capture; and an emission sourceanalyzer comprising a processor, a memory, and a plurality ofprogramming instructions stored in the memory which, when operating onthe processor, cause the processor to: receive signal information fromone or more of the collection platforms; pre-process the signalinformation to perform any combination of normalizing, reducing,analyzing, filtering, or organizing the data; and use the pre-processeddata to: identify one or more radio frequency signals from an emissionsource; and generate radio frequency landscape information for theemission source, including at least: the frequencies of radio frequencysignal emissions from the emission source; and the signal power of radiofrequency signal emissions from the emission source.

According to an aspect of an embodiment, the system further comprises anenriched dataset generator comprising at least a plurality ofprogramming instructions stored in a memory which, when operating on aprocessor, cause the processor to: receive a plurality of datasetscomprising pre-processed signal information and further comprising atleast one common or overlapping characteristic; separate each datasetinto at least a spatial component, a time domain component, and afrequency domain component; map each element of each component of eachdataset to their analogues in each of the other datasets in theplurality of datasets; develop statistics and metrics for one or more ofthe components; and combine the plurality of datasets, their components,and the mapping between them into a single dataset of higher informationcontent than any one of the datasets comprising the plurality ofdatasets.

According to another preferred embodiment, a method for large-area,wide-bandwidth radio frequency signal collection and processing isdisclosed, comprising the steps of: capturing signal informationcomprising radio frequency data and using radio frequency sensor systemson one or more collection platforms; capturing signal informationcomprising metadata about the radio frequency data, including at least atime, a frequency, and a location of capture, using metadata collectorson one or more collection platforms; and receiving signal information atan emission source analyzer from one or more collection platforms;pre-processing the signal information to perform any combination ofnormalizing, reducing, analyzing, filtering, or organizing the data; andusing the pre-processed data to: identify one or more radio frequencysignals from an emission source; and generate radio frequency landscapeinformation for the emission source, including at least: the frequenciesof radio frequency signal emissions from the emission source; and thesignal power of radio frequency signal emissions from the emissionsource.

According to an aspect of an embodiment, the method further comprisesthe steps of: receiving into an enriched dataset a plurality of datasetscomprising pre-processed signal information and further comprising atleast one common or overlapping characteristic; separating each datasetinto at least a spatial component, a time domain component, and afrequency domain component; mapping each element of each component ofeach dataset to their analogues in each of the other datasets in theplurality of datasets; developing statistics and metrics for one or moreof the components; and combining the plurality of datasets, theircomponents, and the mapping between them into a single dataset of higherinformation content than any one of the datasets comprising theplurality of datasets.

According to an aspect of an embodiment, the enriched dataset comprisessufficient information to produce a geographical map of radio frequencysignals in a given area.

According to an aspect of an embodiment, the enriched dataset furthercomprises derivative information allowing production of a geographicalmap of radio frequency signals in a given area supplemented withanalysis and statistics of radio frequency activity.

According to an aspect of an embodiment, the enriched dataset furthercomprises time information allowing production of a geographical mapshowing changes over time in the map of radio frequency signals in agiven area.

According to an aspect of an embodiment, the enriched dataset furthercomprises population density information allowing production of ageographical map showing changes in population density over timerelative to changes over time of radio frequency signals in a givenarea.

According to an aspect of an embodiment, the enriched dataset furthercomprises other non-radio-frequency-signal information allowingproduction of a map showing changes in the othernon-radio-frequency-signal information over time relative to changesover time of radio frequency signals in a given area.

According to an aspect of an embodiment, a radio frequency signaldetected by a sensor system or network of sensor systems triggerscollection of more detailed data, less detailed data, wider area,narrower area, higher resolution, and/or lower resolution by at leastone other sensor system on a related or unrelated collection platform orplatforms.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

The accompanying drawings illustrate several aspects and, together withthe description, serve to explain the principles of the inventionaccording to the aspects. It will be appreciated by one skilled in theart that the particular arrangements illustrated in the drawings aremerely exemplary, and are not to be considered as limiting of the scopeof the invention or the claims herein in any way.

FIG. 1 is a diagram illustrating an exemplary overall systemarchitecture for a large-scale radio frequency signal collection andprocessing system, according a preferred embodiment.

FIG. 2 is a block diagram of an exemplary collection platformarchitecture, according to an aspect.

FIG. 3 is a diagram of an exemplary data center, according to oneaspect.

FIG. 4 is a block diagram illustrating an exemplary system architecturefor a signal pre-processing system, according to one aspect.

FIG. 5 is a diagram illustrating the operation of an enriched datasetgenerator, according to one aspect.

FIG. 6 is a diagram illustrating a variety of radio frequency receiversfeeding into a radio frequency signal data analysis platform, accordingto one aspect.

FIG. 7 is a diagram illustrating multiple bandwidth capability of alarge-scale radio frequency signal system, according to one aspect.

FIG. 8 is a diagram illustrating multiple radio frequency signal paths,according to one aspect.

FIG. 9 is a diagram illustrating portions of the radio frequency signalspectrum, according to one aspect.

FIG. 10 is a diagram illustrating spatial and geographical mapping,according to one aspect.

FIG. 11 is a diagram illustrating frequency mapping, according to oneaspect.

FIG. 12 is a diagram illustrating temporal mapping, according to oneaspect.

FIG. 13 is a diagram illustrating multiple levels of coverage, accordingto one aspect.

FIG. 14 is a diagram illustrating frequency band scanning, according toone aspect.

FIG. 15 is a diagram illustrating changes in signals over time,highlighting problems addressed by the large-scale radio frequencysignal platform of the invention.

FIG. 16 is a diagram illustrating the merging of multiple datasets intoan enriched dataset using a large-scale radio frequency signal platform,according to one aspect.

FIG. 17 is a diagram illustrating resolution enhancement for an enricheddataset.

FIG. 18 is a diagram illustrating directional enhancement for anenriched dataset.

FIG. 19 is a diagram illustrating scale and detail enhancement for anenriched dataset.

FIG. 20 is a block diagram illustrating an exemplary hardwarearchitecture of a computing device.

FIG. 21 is a block diagram illustrating an exemplary logicalarchitecture for a client device.

FIG. 22 is a block diagram showing an exemplary architecturalarrangement of clients, servers, and external services.

FIG. 23 is another block diagram illustrating an exemplary hardwarearchitecture of a computing device.

FIG. 24 is a diagram showing aspects of the current state of the art inradio frequency signal collection and processing.

FIG. 25 is a diagram of aspects of an embodiment of a large-scale radiofrequency signal collection and processing system showing improvementsover the state of the art.

FIG. 26 is a diagram of additional aspects of an embodiment of alarge-scale radio frequency signal collection and processing systemshowing the incorporation of augmentation data and cloud-based datastorage and processing.

DETAILED DESCRIPTION

The inventor has conceived, and reduced to practice, a large-scale radiofrequency signal collection and processing system comprising one or moresensor systems mounted on one or more collection platforms thatintegrates a plurality of overlapping datasets with differingcharacteristics (e.g., different resolutions, different view angles,different heights, different time periods, unrelated types of data) togenerate an enriched dataset or datasets using a variety of processingtechniques (e.g., statistical analysis, signal processing, imageprocessing) that allows for more comprehensive analysis of the radiofrequency signal landscape than would be possible using any of thedatasets individually, or in combination but without such integration.

The enriched dataset would comprise a plurality of dimensions, allowinganalysis of radio frequency signal beyond simple two-dimensionalmapping. Using an analogy from satellite imagery, a single pass of asatellite from a high angle over a particular area might produce arelatively low-resolution two-dimensional image of that area. Certainfeatures would be distinguishable, for example, roof tops, but otherfeatures might not be distinguishable because of the low-resolution, forexample, air conditioning units on top of buildings. Further passes ofthe same satellite (or a different satellite with similar receivingcharacteristics) could be combined to produce a higher resolutiontwo-dimensional image of that area, allowing previouslyundistinguishable features, such as the air conditioning units on top ofbuildings, to be distinguished. Satellites imaging the same area, butfrom a lower angle, would reveal characteristics that were notdistinguishable from the higher angle, such as the sides of buildings,adding a third dimension to the data by extrapolation of buildingheights, etc. Further detail could be added by images from otherdatasets such as street level mobile phone images, or images on theinternet with photos of certain buildings (for example, historicalbuildings). Additional dimensions could be added to the enriched datasetby integrating non-spatial data for the area. For example, a fourthdimension could be added showing the changes in data over time. A fifthdimension for analysis could be created by integrating population datafor the same area, allowing population levels to be mapped onto thefour-dimensional model from satellite imagery. Additional dimensionscould be added integrating further datasets associated with that area.

In a manner similar to the analogy, enriched datasets can be created forradio frequency signals. High angle aerial or satellite receivers cancreate a map in a single pass of an area. Additional passes can becombined to produce refined levels of detail for that area. Receptionfrom different angles can be used to identify directional radiofrequency signals, creating a more representative map of the radiofrequency signal coverage in the area. Lower altitude and ground-basedreception can be used to provide a high level of detail and additionaldirectional and localization information. These emissions can be trackedover time, creating a fourth dimension in the dataset. Additionaldimensions can be added from processing and analysis of the radiofrequency signals and/or derivative data (e.g., statistics on usage,coverage, technologies deployed, activity, etc.), and/or from disparatedatasets, such as population data, telecom provider information, andother types of data.

In a manner that is the inverse of the satellite imagery analogy,enriched datasets can be created for radio frequency signals using localsensors that cue other sensors. For example, a local sensor mightperceive a change in the radio frequency signal environment. This changemight cause other sensors on a platform or a variety of differentplatforms to change sensor and/or platform behavior in response thesensed change in the radio frequency signal environment.

By integrating these various dimensions into an enriched dataset,greater insight can be gained from analyzing the data than from the useof individual datasets, or even from the use of combined datasets thatare not so integrated. As one example, in order to determine potentialsignal interference for a certain application, it might be necessary toknow the signal characteristics, location, and emission times of allemitters with similar signal characteristics in a particular area over acertain period of time. No single dataset or simple combination ofdatasets is likely to contain such information in a form useful foranalysis, but an enriched dataset that integrates multiple datasets forsuch signals, containing such characteristics such as location ofemission, direction of emission, and signal characteristics of emissionwould allow for the extrapolation of changes over time necessary forthat application.

One or more different aspects may be described in the presentapplication. Further, for one or more of the aspects described herein,numerous alternative arrangements may be described; it should beappreciated that these are presented for illustrative purposes only andare not limiting of the aspects contained herein or the claims presentedherein in any way. One or more of the arrangements may be widelyapplicable to numerous aspects, as may be readily apparent from thedisclosure. In general, arrangements are described in sufficient detailto enable those skilled in the art to practice one or more of theaspects, and it should be appreciated that other arrangements may beutilized and that structural, logical, software, electrical and otherchanges may be made without departing from the scope of the particularaspects. Particular features of one or more of the aspects describedherein may be described with reference to one or more particular aspectsor figures that form a part of the present disclosure, and in which areshown, by way of illustration, specific arrangements of one or more ofthe aspects. It should be appreciated, however, that such features arenot limited to usage in the one or more particular aspects or figureswith reference to which they are described. The present disclosure isneither a literal description of all arrangements of one or more of theaspects nor a listing of features of one or more of the aspects thatmust be present in all arrangements.

Headings of sections provided in this patent application and the titleof this patent application are for convenience only, and are not to betaken as limiting the disclosure in any way.

Devices that are in communication with each other need not be incontinuous communication with each other, unless expressly specifiedotherwise. In addition, devices that are in communication with eachother may communicate directly or indirectly through one or morecommunication means or intermediaries, logical or physical.

A description of an aspect with several components in communication witheach other does not imply that all such components are required. To thecontrary, a variety of optional components may be described toillustrate a wide variety of possible aspects and in order to more fullyillustrate one or more aspects. Similarly, although process steps,method steps, algorithms or the like may be described in a sequentialorder, such processes, methods and algorithms may generally beconfigured to work in alternate orders, unless specifically stated tothe contrary. In other words, any sequence or order of steps that may bedescribed in this patent application does not, in and of itself,indicate a requirement that the steps be performed in that order. Thesteps of described processes may be performed in any order practical.Further, some steps may be performed simultaneously despite beingdescribed or implied as occurring non-simultaneously (e.g., because onestep is described after the other step). Moreover, the illustration of aprocess by its depiction in a drawing does not imply that theillustrated process is exclusive of other variations and modificationsthereto, does not imply that the illustrated process or any of its stepsare necessary to one or more of the aspects, and does not imply that theillustrated process is preferred. Also, steps are generally describedonce per aspect, but this does not mean they must occur once, or thatthey may only occur once each time a process, method, or algorithm iscarried out or executed. Some steps may be omitted in some aspects orsome occurrences, or some steps may be executed more than once in agiven aspect or occurrence.

When a single device or article is described herein, it will be readilyapparent that more than one device or article may be used in place of asingle device or article. Similarly, where more than one device orarticle is described herein, it will be readily apparent that a singledevice or article may be used in place of the more than one device orarticle.

The functionality or the features of a device may be alternativelyembodied by one or more other devices that are not explicitly describedas having such functionality or features. Thus, other aspects need notinclude the device itself.

Techniques and mechanisms described or referenced herein will sometimesbe described in singular form for clarity. However, it should beappreciated that particular aspects may include multiple iterations of atechnique or multiple instantiations of a mechanism unless notedotherwise. Process descriptions or blocks in figures should beunderstood as representing modules, segments, or portions of code whichinclude one or more executable instructions for implementing specificlogical functions or steps in the process. Alternate implementations areincluded within the scope of various aspects in which, for example,functions may be executed out of order from that shown or discussed,including substantially concurrently or in reverse order, depending onthe functionality involved, as would be understood by those havingordinary skill in the art.

Definitions

“Augmentation data” as used herein is non-radio-frequency-signal datathat may be combined with radio frequency signal data and metadata tofurther describe the radio frequency landscape and its context.Augmentation data may include, but is not limited to, demographic data(e.g. population density, housing density, income levels, etc.), realestate valuations, traffic data and patterns, building heights andlocations, and terrain information.

“Data center” as used herein refers to any location used for storing andprocessing collected radio frequency signal data. The term data centerdoes not exclude using a collection platform and/or sensors on aplatform as the location for storing an processing the collected radiofrequency signal data.

“Cloud-based” as used herein refers to any computing or data storagethat takes place over a network of computing devices that are connectedto, and accessible through, the internet.

“Collection platform” or “platform” as used herein refers to a surface,structure, vehicle, portable electronic device, or other object on whicha sensor system may be mounted, in which a sensor system may be placed,or into which a sensor system may be integrated. Collection platformsmay take a variety of forms, including, but not limited to fixed objects(e.g. desks, windows, towers, buildings, billboards, etc.), portableobjects (backpacks, mobile phones, etc.), vehicles (cars, trucks, boats,etc.), small scale aircraft (e.g., drones, model airplanes, etc.),aircraft (airplanes, helicopters, etc.), balloons (hot-air balloons,weather balloons, etc.), and satellites.

“Emission source” as used herein means the source of a radio frequencysignal emission.

“Landscape” or “radio frequency landscape” as used herein means thetotality of radio frequency signal activity in a given area, including,but not limited to, signal times, signal locations, signal directions,signal altitudes, signal sources, signal frequencies, signal powers,areas of good reception, and areas of poor reception, and may includeaugmentation data.

“Metadata” as used herein means information describing the radiofrequency data (for example, information about the sensor system andcollection platform configuration, the area over which radio frequencydata was collected, the time at which data was collected, look-angles ofthe sensor system or collection platform, operating characteristics ofthe sensor system or collection platform, including but not limited to,location, speed, orientation, movement relative to Earth, movementrelative to other sensor systems, frequencies of operation, calibrationdata, times of operation, etc.), inputs from other algorithms, and/orinputs from other sources of information (for example, a database orlist of cellular base station locations).

“Natural” or “naturally occurring” as used herein means a radiofrequency signal that is of natural origin. For example, impulsive radiofrequency signals generated by lightning.

“Radio frequency” as used herein means frequencies from about 3kilohertz (3 kHz) to about 300 gigahertz (300 GHz).

“Radio frequency data” means any data associated with a radio frequencysignal.

“Radio frequency signal” as used herein means any detectableelectromagnetic radiation with a frequency or frequencies from about 3kilohertz (3 kHz) to about 300 gigahertz (300 GHz).

“Sensor system” as used herein means a system capable of receiving radiofrequency signals. Although some of the embodiments herein assume acomplex sensor system such as a software-defined radio capable ofdetecting, receiving, processing, and storing radio frequency signals,the term sensor system is not so limited, and includes any systemcapable of receiving radio frequency signals from simple wire antennareceivers to sophisticated systems with directional antennas and complexcircuitry.

“Signal” as used herein means radio frequency signal unless the contextindicates otherwise.

“Signal information” means any information collected about a signal orused to describe a signal. This may include radio frequency data andmetadata describing the radio frequency data (for example, informationabout the sensor system and collection platform configuration, the areaover which radio frequency data was collected, the time at which datawas collected, look-angles of the sensor system or collection platform,operating characteristics of the sensor system or collection platform,including but not limited to, location, speed, orientation, movementrelative to Earth, movement relative to other sensor systems,frequencies of operation, calibration data, times of operation, etc.),inputs from other algorithms, and/or inputs from other sources ofinformation (for example, a database or list of cellular base stationlocations). Signal information may be from one or more geographicallocations or times.

“Enriched data” or “enriched dataset” as used herein means thecombination, integration, correlation, convolution, de-convolution,and/or superimposition of a plurality of datasets with at least onecommon or overlapping characteristic in order to create a larger datasetallowing for more complex analysis than could be performed using theindividual datasets that make up the enriched dataset. For example, thesuperimposition of several data sets from different perspectives may beused to create a new perspective on a scene or a unique interpretationof a scene. Integration of multiple lower resolution datasets (whetherspatial, frequency, or time) may be used to obtain higher resolutiondata for the subject being studied.

“Synthetic” as used herein means a radio frequency signal that is ofhuman origin. Note that not all synthetic signals are telecommunicationsignals. Many electronic devices (for example, microwave ovens) generateradio frequency signals that are not intended to convey information.

“Telecommunication signal” or “telecom signal” means any radio frequencysignal carrying information intended to convey a message.Telecommunications signals may be analog or digital. Althoughtelecommunication signals are frequently associated with mobile phonesand mobile phone companies, the term as used herein is broader, andencompasses any form of synthetic radio frequency signal intending toconvey information, including, but not limited to, radio, television,mobile phone, WiFi, Bluetooth, or other such transmission.

FIG. 1 is a diagram illustrating an exemplary overall systemarchitecture for a large-scale radio frequency signal collection andprocessing system 100, according to a preferred embodiment. One or morecollection platforms 200 with one or more sensor systems 210 may be usedto receive, process, and/or transmit radio frequency data, associatedmetadata, and mappings to other collection platforms, sensor systems,and/or data centers 300, which may be local, remote, or cloud-based.Possible collection and processing may include collection of moredetailed data, less detailed data, wider area, narrower area, higherresolution, and/or lower resolution by at least one other sensor systemon a related or unrelated collection platform or platforms, if a radiofrequency signal is detected by a sensor system or network of sensorsystems. The data centers 300 may then perform a number of operations oncollected information from multiple collection platforms 200 such aspre-processing of data, generation of enriched datasets, or modeling anddeep analysis of data. In this manner it can be seen that radiofrequency signal data collection flows from individual sensor systems210 on each collection platform 200, from individual collectionplatforms 200 ultimately into data centers 300. Further, in someembodiments, collection platforms 200 may communicate directly with eachother or through data centers 300 for processing to be accomplished atdata centers 300 and/or at collection platforms 200 using data frommultiple collection platforms 200. This effects a hierarchicalcollection process wherein many sensors feed data into a large-scalecollection system that may be distributed across a wide area, with eachcomponent receiving and processing data before passing it to the nextcomponent for additional processing with other collected data.

FIG. 2 is a block diagram of an exemplary architecture of a collectionplatform 200, according to an aspect. The collection platform 200 maycomprise one or more radio frequency sensor systems 210 that receiveradio frequency signals from various natural or synthetic sources (forexample, transmissions from cellular towers, mobile devices, satellites,natural events, and other sources of radio frequency signal). Eachsensor system 210 may comprise a receiver 211 such as an antenna orsystem of antennas, for example a single antenna that may be tuned to aspecific frequency band or may be a wide-band or configurable antennathat may be modified during operation to receive on differentfrequencies, or an array of antennas to provide coverage of multiplefrequencies or areas (for example, multiple directional antennasoriented to receive from different geographical areas); radio frequencyelectronics that may condition the radio frequency signal to enhance orsuppress certain characteristics, convert radio frequency signal to adifferent frequency, filter radio frequency signal, and/or perform otheranalog processing. For mobile collection platforms, and particularlyairplanes which emit a strong, intermittent UAT and/or ADS-B locatorsignal, the receiver 211 may be configured to filter out the certainsignals or interference from the platform, such as the ADS-B signal.Signals received by receivers 211 may be converted from analog emissionwaveforms to digital signals for processing, for example using signalanalog-to-digital converters. Signal processing may occur at a varietyof stages. For example, in some embodiments, the sensor system 210 maycomprise a software-defined radio (SDR), which may have an on-boardpre-processor 212 configured to perform any of a number of operations onthe digitized signals before optionally sending signal output to aplatform processor 220 that may perform additional pre-processing ondata from multiple sensor systems 210 before transmitting the data to adata center 300. Collected signal information may be stored by platformprocessor 220 in a limited, on-board storage 230 such as for short-termstorage of bulk signal data (for example, to enable a variety ofanalysis operations that may involve historical data) or short-termstorage of processed data, or may be processed with data from othersensors, or may be transmitted via a data transmitter 240 as output todata centers or other sensors. It should be appreciated that any numberof sensor systems 210, signal receivers 211, and pre-processors 212 maybe utilized according to various arrangements and configurations, andthe quantity and arrangement shown are merely exemplary. A variety ofmetadata collectors 250 may be on the collection platform. Metadatacollectors 250 may be global positioning system (GPS) devices, vehiclespeedometers, airspeed indicators, accelerometers, gyroscopic sensors,angle sensors, map databases, or any other device or system thatprovides contextual information about the radio frequency signal databeing gathered. While shown here as separate devices from the sensorsystems, metadata collectors 250 may be part of the sensor systems 210,themselves, and may provide information such as frequencies ofoperation, calibration data, times of operation, or other informationthat provides contextual information about the radio frequency signaldata being gathered. Metadata from the metadata collectors 250 may beprocessed by an onboard platform processor 220 to combine it with theradio frequency data with which it is associated, or it may be passedthrough to storage 230 or a data transmitter 240.

FIG. 3 is a diagram of an exemplary data center 300, according to oneaspect. Input from a number of collection platforms 200 or from a numberof sensor systems 210 may be received at a data center 300 and processedthrough a pre-processing engine 400. Incoming data may be stored 310 foroutput to other processes. The pre-processing engine 400 may use avariety of algorithms to perform operations on collected data, forexample including (but not limited to) filtering sensor data to removeunwanted data (for example, removing noise to clean up the dataset, ordiscriminating between unwanted signals and those of interest), runningcompression algorithms to reduce the size of data, and sorting portionsof the sensor data based on factors such as frequency, intensity,duration, location, or other metrics. The modeling or deep analysisengine 320 may use a variety of algorithms to analyze the data toprovide useful information to separate synthetic from naturallyoccurring signals, to determine the location, strength, and othercharacteristics of radio frequency signal data, the location, signalcharacteristics, and usage characteristics of telecommunicationssignals, telecom signals information such as frequency, bandwidth,modulation, efficiency, coverage, usage, location, and othercharacteristics.

Sensor data may be integrated from multiple sensors to produce complexdatasets, for example to combine sensor data and produce a more completedataset when data from only one or some of the sensors may beinsufficient, or may be sufficient to answer some questions but have thepotential to provide useful insights when combined with additional data.For example, combining signal strength in an area (cell coverage) withcensus information (population) to reveal “MHz-pop”, a metric used intelecom applications. Mappings may be produced to correlate sensor dataand produce derived insights, such as (for example, including but notlimited to) spatial mapping to correlate signals with geospatiallocations (such as identifying where signal sources are located, orareas of frequency band usage, or other such location-based informationand analyses), time mapping to identify signal information over time(such as identifying when signals peak or overlap, or determining bandutilization during certain time windows, for example), and frequencymapping to correlate sensor data over multiple frequencies and overcomepotential shortcomings of using one or some sensors instead of the fulldataset available from multiple collection platforms 200 (for example,to correlate information from multiple sensors detecting signals withina desired frequency band at different times, to form a more completedetection over a span of time instead of having detection gaps). Thedata may be processed through a number of algorithms at this stage tofilter and sort the data for further analysis including (but not limitedto) bin-wise processing that may be used to process signals over anumber of frequency domain samples, for example using fast Fouriertransform (FFT) frequency bins. Bin-wise processing operations mayinclude (but are not limited to) mean, median, maximum, minimum, orother statistical value of a signal's amplitude, or the log of theamplitude, or of the power in each frequency domain sample. Frequencybins may be treated as the smallest non-time-series functional unit ofradio frequency signal during processing, and bins may be groupedtogether as needed into blocks that may be processed as a single unit(for example, collapsing multiple adjacent bins into a single largerfrequency block using any or a combination of minimum, maximum, mean,median, standard deviation, variance, skew, kurtosis, or otherstatistical measure). Additionally, calibration information for anynumber of sensors may be incorporated (if available), to improveanalysis and detection of features such as low or negativesignal-to-noise ratio energy, or absolute measurements of amplitude orpower, which may be heavily-dependent on the calibration of the sensorsproviding data (for example, a poorly-calibrated sensor may misrepresentenergy levels or simply miss low-energy signals that would have beendetected if properly calibrated).

Data samples may incorporate some degree of preset or configurableoverlap. Weak signal detection may be used to determine if the radiofrequency signal data for any bin or time-series sample needs to bestrengthened, for example to raise it above a noise floor or otherthreshold by using statistical techniques, to develop training data andapply machine learning techniques to datasets, for example toincorporate image analysis techniques based on sensor data mappings.Enriched dataset generation may be used to map data of differentspatial, temporal, or frequency resolutions to produce an enricheddataset by combining multiple datasets or data from different sensorsystems and/or collection platforms. Signal analysis may be used toanalyze signals across a large number of frequency bands, for example todetermine band utilization of a signal by analyzing the signal toestimate the fraction of time and frequency that the signal is above anoise floor for the respective frequency band. This may then be used todetermine overall band utilization based on analysis of multiple signalswithin a frequency band and comparing against the band capacity or toapply enriched data to estimate geographical density of signal sources.

FIG. 4 is a block diagram illustrating an exemplary system architecturefor a signal pre-processing engine 400, according to one aspect. Thepre-processing engine 400 may perform limited processing of the digitalradio frequency signal data to reduce, analyze, filter, and/or organizethe data. The pre-processing engine 400 may use a variety of algorithmsto perform operations on collected data, for example including (but notlimited to) using signal filters 410 to remove unwanted data (forexample, removing noise to clean up the dataset, or discriminatingbetween unwanted signals and those of interest), running compressionalgorithms 420 to reduce the size of data, and applying sortingalgorithms 430 to sort portions of the sensor data based on factors suchas frequency, intensity, duration, location, or other metrics.

FIG. 5 is a diagram illustrating the operation of an enriched datasetgenerator 500, according to an aspect. Enriched dataset generator 500may be used to combine sorted data into larger datasets that are used toreveal additional insights that would not be apparent from any singleportion of data alone. A plurality of datasets 504 may be received froma pre-processing engine 400, for example each set may correspond to aparticular sensor system, set of sensor systems, collection platform, orcollection platforms, or other selection of radio frequency signal ornon-radio frequency signal input data. A component separator 502 maythen separate specific data components from each of the incomingdatasets 504, separating out spatial, time domain, and frequency domaindata components 505. These separate components may then be processedthrough a mapping engine 501, which maps between the individualcomponents 505 from each of the multiple datasets 504, and then providesthe mapping information to a data reintegrator 503, which combines themultiple datasets 504 using the mapping information to produce a singleenriched dataset as output.

FIG. 24 is a diagram showing aspects 2400 of the current state of theart in radio frequency signal collection and processing. In the currentstate of the art, data may be gathered from a plurality of collectionplatforms 2410 a-n. The platforms comprise radio frequency sensors 2420a-n and local processors 2430 a-n. The local processors 2430 a-n may belocated on-board the collection platforms 2410 a-n, as shown in thisdiagram, or may be located off of the collection platforms 2410 a-n.Data received by the sensors 2420 a-n is processed locally using thelocal processors 2430 a-n, and results 2440 a-n are obtained prior toforwarding to remote locations for aggregation. The local processors2430 a-n may be located on-board the collection platforms 2410 a-n, asshown in this diagram, or may be located off of the collection platforms2410 a-n, but in either case, the data received by the sensors on aparticular platform is processed prior to forwarding to remotelocations. The method of processing the raw/source data locally,obtaining results, and forwarding the results, is a simple aggregationprocess 2450 that prevents sophisticated analysis of the raw/source datareceived from the sensors. A tremendous amount of information containedin the raw/source data is lost during the process.

FIG. 25 is a diagram of aspects 2500 of an embodiment of a large-scaleradio frequency signal collection and processing system showingimprovements over the state of the art. In this embodiment, data aregathered from one or more collection platforms 2510 a-n. The collectionplatforms comprise sensor systems 2520 a-n, metadata collectors 2540a-n, and optionally pre-processors 2530 a-n. Radio frequency signal dataare obtained using the sensor systems 2520 a-n, along with metadata frommetadata collectors 2540 a-n, which further describes the radiofrequency signal data. The data and metadata may be pre-processed usingpre-processors 2530 a-n to normalize, reduce, analyze, filter, ororganize the data for later processing, or may be passed directly a datacenter or cloud-based network for geospatial processing 2550.Importantly, the raw/source and/or pre-processed data are not degradedduring this process, and raw/source and/or pre-processed data from alarge number of collection platforms 2510 a-n may be combined intoenhanced datasets. The enhanced datasets allow for complex processing ofmulti-platform datasets, resulting in extraction of additionalinformation 2560 that is not available from simple locally-processedresult aggregation, and. Because the information content from theraw/source and/or pre-processed data is still available, a great deal ofadditional information can be extracted using a variety of dataprocessing algorithms. Information extraction can be further enhanced bythe use of machine learning algorithms which can identify patterns inthe data before or after processing by other algorithms. The larger thecollection of data, the more information and detail can be extracted bysophisticated processing methods.

FIG. 26 is a diagram of additional aspects 2600 of an embodiment of alarge-scale radio frequency signal collection and processing systemshowing the incorporation of augmentation data and cloud-based datastorage and processing. As previously described, radio frequency signaldata and metadata may be obtained from various data sources 2610,including but not limited to sensor systems, metadata collectors, orarchives. The data thus obtained are input into the system 2620 andstored in data storage that allows for data aggregation, such ascloud-based storage 2650. Augmentation data may be obtained fromaugmentation data sources 2630 such as business partners, publicrecords, crowdsourcing, etc. Augmentation data are also input into thesystem 2640 and stored in the cloud-based data storage 2650. A pluralityof processing systems 2660 a-n may have access to the cloud-based datastorage 2650 and process the data to extract different kinds ofinformation about the radio frequency landscape. These results can beused to generate data products 2670 useful to customers in variousindustries.

These additional aspects 2600 of the system may be used to extract orproduce information about the radio frequency landscape that couldotherwise be obtained. One example is the use of the system to trackvaluations of radio frequency equipment over time. For example, considera piece of real estate located in a rural area, on which a single cellphone tower is located. The value of the property (or value of leasingthe property) is augmented data (i.e., not radio frequency signalinformation), which can be associated with radio frequency signal data.Additionally, changes in the value of the property can be associatedwith changes in the radio frequency equipment and radio frequency usageon that property over time. If the area in which the property is locatedbecomes urbanized, not only will the value of the property change, butit is likely that additional cell phone towers or other radio frequencyequipment will be installed, and the usage profile of the radiofrequencies will change. Changes in a wide variety of informationrelative to the property may be tracked, including such things as thenumber of operators using a tower, the specific operators using thetowers, the value of the rent for having a cell tower on a building,value of the land leased by a tower company, and a wide variety ofratios and relationships can be created between the radio frequencysignal information and the augmented data information. Broader changescan be tracked, as well, such as changes in the density of towers in agiven area, which can reveal growth in population density. Relativechanges in radio frequency usage can be tracked, such as installation of4G sites where 3G used to be, or installation of 4G sites where 4G sitesdominated.

Detailed Description of Exemplary Aspects

FIG. 6 is a diagram illustrating a variety of radio frequency signalcollection platforms 200 which may feed data into the overall system,according to one aspect. According to the aspect, the system asconceived herein would be most effective if a broad range of radiofrequency signal data is collected from a variety of platforms withdifferent characteristics as shown at 600. Ideally, the system wouldconsist of a plurality of mobile platforms and stationary platforms. Aplurality of different types of sensors would ideally be mounted on aplurality of different platforms such as satellites 606, airplanes 605,cars, or balloons 604, each of collects data using differentinstrumentation, from different altitudes, and in overlapping ordistinct geographical areas. Stationary receivers would also be of avariety of types such as non-directional 602, directional 601, 603 andwould be located in overlapping areas of coverage, or in geographicallydiverse locations, at different altitudes, and/or with different linesof sight. Groups of collection platforms may be used, for example invehicle fleets such as cab or ridesharing services, aircraft operated byan airline, weather balloons, boats, or other fleet types orarrangements.

FIG. 7 is a diagram illustrating multiple bandwidth capability of alarge-scale radio frequency signal system, according to one aspect. Asshown, a mapping 700 across multiple frequency bands may be used toidentify or confirm a signal of interest 701 using sensors withdifferent sensitivities and capabilities. Any one sensor may pick up asignal of interest 701, but the signal could be weak or indistinct. Bycombining sensors with different characteristics, for example, awide-band sensor 710, medium-band sensor 720, and narrow-band sensor730, a signal of interest 701 that might otherwise have been missed orindistinct can be confirmed or clarified.

FIG. 8 is a diagram illustrating multiple radio frequency signal paths,according to one aspect. As shown, a radio frequency signal in onedirection 810 may be blocked or otherwise interfered with, for exampleby a building 803 that blocks the line-of-sight between an emissionsource 801 and receiving platform 802. In such cases, an alternatereceiving platform may be available, such as a drone 804. By utilizingthe large-scale architecture of the system 100, drone 804 may receivethe blocked emission from a different direction 820 and thus feed itinto the system 100 for use. In this manner it may be appreciated thatthe use of large-scale implementation with multiple sensors 210installed on a variety of platforms (as shown in FIG. 6 ) enables morecomplete collection and analysis of emissions through the collection andcorrelation of multiple sensor data sources to overcome any shortcomingsof, or interference with, any particular sensor.

FIG. 9 is a diagram illustrating portions of the electromagneticspectrum 900 that may be collected by the system 100, according to oneaspect. The electromagnetic spectrum 900 comprises a wide variety offrequency bands that characterize radio frequency signals, withfrequencies from about 3 kHz to 300 GHz being grouped into the radiospectrum 901 and frequencies above 300 GHz corresponding to infrared,visible light, ultraviolet, x-rays, and gamma rays. According to variousaspects and arrangements, a large-scale radio frequency signalcollection platform may be used to collect and analyze sensor dataacross any number of frequency bands in the radio frequency spectrum901. For example, sensor data may be collected across a variety of radiofrequency bands, and used to form more complex mappings that may yieldadditional analysis insights by incorporating information traditionallyoverlooked (or simply not available) as efforts are focused on certainportions of the radio frequency signal spectrum. As described previously(with reference to FIG. 4 ), individual frequency bins may be condensedinto larger frequency blocks to enable handling as a single unit,allowing mapping and analysis operations to have adjustable granularitywithin the overall radio frequency signal spectrum and enablingefficient processing of both very fine, and very large, datasets.

FIG. 10 is a diagram illustrating spatial and geographical mapping,according to one aspect. As shown, mapping received signal and locationinformation from sensors enables the correlation of emissions withgeospatial regions where they occur (or from which they were emitted).Specific emissions may exist at certain frequencies 1020 a-n (shown hereas “Freq A”, “Freq B”, and “Freq C”) and in certain geographic areas1010 a-n. Sometimes, emissions may overlap in some areas 1030 a-n,indicating regions with multiple emission frequencies. It can beappreciated from the illustration that such spatial mapping enablesrapid identification of both areas with one or more frequency bands, aswell as frequency bands that occur in multiple regions. This mapping maybe produced (for example) by a single collection platform 100 bycorrelating information from multiple sensors (for example, directionalantennas that may receive on similar frequency bands in differentregions, or that may receive on different frequency bands in the samelocation), or by a data center 300 by correlating information receivedfrom multiple, spatially-distinct collection platforms (for example,correlating information received from collection platforms in differentregions but at the same frequencies).

FIG. 11 is a diagram illustrating frequency mapping 1100, according toone aspect. As shown, a frequency mapping 1100 may be used to correlatesensor data for frequencies 1110 versus signal power 1120 for a givenregion, indicating the respective intensity 1101 of a given frequencyand enabling power-based analysis or analysis of specific frequencybands within the selected region.

FIG. 12 is a diagram illustrating temporal mapping 1200, according toone aspect. As shown, a temporal mapping 1200 may be used to correlatesensor data for the number of signals of that frequency in a given area1220 over time 1210, to identify a current level 1201 of usage of agiven frequency in a particular location over a span of time. This maybe used to identify how a frequency band is being utilized, such asperiod of high-usage that may indicate the presence of additionaltransmissions or interference that may be saturating the band andinterfering with desired signal emissions.

FIG. 13 is a diagram illustrating multiple levels of coverage, accordingto one aspect. As shown, various collection platforms (as discussedabove, with reference to FIG. 6 ) may be used to incorporate therespective strengths of various platforms. For example, a stationaryplatform on a tower 1301 may have very high data transfer rate but asmall detection area due to the stationary nature of the installation.Mobile platforms such as those installed on vehicles 1302 may haveincreased physical coverage as the platform can move around, butdecreased data rate, for example, due to having to store data on a harddrive on the vehicle and remove the hard drive to manually transfer thedata at certain intervals, or by having to transmit the data through acellular connection. An airborne platform installed on an aircraft 1303may have a much larger area still, with the same data rate issues asground-based vehicles. A satellite-based platform 1304 would have thewidest possible coverage at the expense of data transfer rate due tohardware limitations of the satellite or transmission requirements.

FIG. 14 is a diagram illustrating a problem associated with frequencyband scanning where the sensor is scanning from a moving platform,according to one aspect. A common method of scanning a broad spectrum ofradio frequency signals with a single receiver is to successively scannarrow ranges of frequencies over short intervals. For example, certaincurrently-available software defined radios (SDRs) can scan a spectrumfrom 70 Mhz to 6 GHz, and do so by successively scanning small frequencybands for brief periods of time, as defined by their programming. Forexample, if an SDR is programmed to scan the entire 70 MHz to 6 GHzspectrum in 20 MHz frequency bands for 67 ms in each band, it wouldrequire approximately 20 seconds to scan the entire spectrum. While thisis an efficient method of scanning a broad spectrum with a singlereceiver, such a receiver mounted on a fast, mobile platform like anairplane would travel a significant distance during that time. Thus, asthe platform travels during scanning, it will be scanning a slightlydifferent geographical area for each frequency band. However, the use ofa mobile scanner 1410 (such as a vehicle-based sensor on a car oraircraft) may miss detection of the signal of interest as it moves. Anygiven sensor scan 1411 may include a particular frequency band at thattime, but as the platform moves it changes the band being scanned foremissions, resulting in historical scans 1412 a-n that may each cover adifferent physical area, or a different frequency band, or both. Thisintroduces complexity and errors due to incomplete scan coverage, as asignal of interest may require a scan to be in the right place at theright time, while also scanning the correct frequency band. For example,at time T1 1401, the mobile platform 1410 is scanning frequency band F11402 at location L1 1403. A signal F2 1404 transmitted at that same timeand location would not be detected. The signal F2 1404 can only bedetected by the scanner on the mobile platform 1410 at time T2 1405 atlocation L2 1406. Using a single mobile scanner 1410 is insufficient toreliably detect the signal of interest without performing many repeatedsensor sweeps at an offset, to ensure that eventually all possiblecombinations of frequency, time, and location have been checked.

FIG. 15 is a diagram illustrating a solution to the problem of scanningfrom a mobile platform, according to one aspect, which is to combine thedata from the mobile platforms with data from non-mobile platforms togenerate an enriched dataset. As shown, any given sensor may be scanningat a particular time 1510, 1520 and in a certain location 1530, 1540.Both mobile scanners 1501 and stationary scanners 1502 may be used, andtheir respective sensor data may be collected and combined into a singleenriched dataset 1550 a-n, comprises the combined data from multiplescanners. In this way, the shortcomings of any particular scanner may beovercome by the combined data from another scanner, for example a mobilescanner 1501 may be changing position while also changing scan frequencyover time, while a stationary scanner 1502 remains in one location whilechanging frequency over time (thus ensuring that, at the location of thestationary scanner, data is present for all scanned frequencies). Anenriched dataset 1550 a-n may comprise any combination of sensor datafrom any number of sensors, for example multiple sensors may be combinedto form more complete geographical coverage of an area, multiplefrequency bins may be combined to create a dataset encompassing a wideblock of the radio frequency signal spectrum at a given time or in agiven area, or other such combinations. This enables working with datafrom many sensors or collection platforms as a singular unit, enablingadvanced analysis over large datasets that comprise many data pointscollected at a high level of granularity, such that detail is not lostwhen combining data into an enriched dataset 1550 a-n.

FIG. 16 is a diagram illustrating changes in signals over time,highlighting a different, but related, problem with scanning a broadspectrum of radio frequency signals using a single receiver. As shown,within a given frequency band 1600 there may be changes in emissionintensity over time. If a peak in intensity occurs at a particular time1601, detection requires that a sensor be tuned to the frequency band atthat time, and also receiving in the location where the emission ispresent. By using multiple sensors or collection platforms, alarge-scale radio frequency signal collection platform can improvedetection by ensuring that at any given time, there is a sensor in agiven area receiving on a given frequency 1602. Rather than relying on asingle sensor to be receiving on the right frequency in the right placeat the right time, multiple sensors may be offset and their respectivedata combined (each receiving on a separate frequency band at the sametime in the same area), so that their combined sensor data comprises amore complete dataset that may be analyzed as a whole, ensuring that asignal of interest is properly detected.

FIG. 17 is a diagram illustrating resolution enhancement 1700 for anenriched dataset. To improve the resolution (that is, the level ofgranularity or detail available in the data) of an enriched dataset,multiple individual low-resolution datasets 1701-1703 may be collected.These may be, for example, multiple scanning passes from a single sensor(as illustrated), or single passes from each of multiple sensors, orother arrangements that create multiple datasets that comprise similardata (for example, multiple sensors scanning the same geographicalarea). These disparate datasets may then be processed using any of avariety of statistical, signal, and/or image processing algorithms 1710.The resulting post-processed data may then be combined into a single,high-resolution dataset 1720, from which additional details and datainsights may be drawn. This technique applies processing methods to datato reveal additional detail that would be missed using existing datahandling techniques, by interacting with the underlying data in itsoriginal form to utilize the benefits of combined analysis.

FIG. 18 is a diagram illustrating directional enhancement 1800 for anenriched dataset. Some emitters, such as cellular base transceiverstations (BTSes), commonly known as cell towers, emit asymmetricpatterns such as a flattened torus or other horizontal emission pattern1810. This creates issues when a collection platform is at a high angleabove the emitter 1801, where the platform may not receive a signal asit is outside the emission pattern. However, another platform may be ata shallower angle 1802 that places it within the emission pattern, andthis second platform will therefore receive signals from the emitter1810. By combining the data from each of these platforms into anenriched dataset, not only will the signal be included in the dataset(when it would have otherwise been missed in any dataset from the first,steep-angle platform), but due to the directional nature of theemissions and positioning of the platforms it becomes possible to form a3D model of the emission patterns and signal directions 1820. Thisenables the detection of signal presence, identification of signal type,and directionality, as well as the precise locating of emission sourcesthrough techniques such as (for example) signal triangulation usingmultiple platforms.

As another example, directional enhancement may be used to geolocate asignal. Another example is geolocating a signal by fusing multiplemeasurements together to locate an emitter. These multiple measurementscan be from a single sensor making measurements over a period of time,or from multiple sensors possible on multiple different types ofplatforms taking measurement at the same time or not at the same time,and combining the measurements.

FIG. 19 is a diagram illustrating scale and detail enhancement 1900 foran enriched dataset. When multiple platforms are used at multiple levelsof scale, signals may be received at one platform and used to direct thebehavior of additional platforms at lower scales 1910. For example, asatellite platform 1901 which has a very large area but low level ofdetail (resolution), may receive a signal while scanning a nation-scalearea as shown. This may be used to direct an aerial platform 1902 with asmaller scale but higher level of detail, to scan the area where thesignal was detected in order to more precisely locate the source. Thisin turn may be used to direct mobile 1903 and fixed 1904 groundplatforms to scan within the region, zeroing-in on the emission sourceusing increasingly fine-grained scanning from platforms at smallerscales. This provides the ability to rapidly locate the precise sourceof emissions, while scanning large areas rapidly using low-detail,large-area scanners rather than having to scan a large area withhigh-detail scanners. Conversely, a local, fixed ground platform mightsense a change in the signal environment. This change could act as a cueto sensors on airborne or satellite platforms to reconfigure, repoint,or reposition their sensors.

Computer Architecture

Generally, the data processing techniques disclosed herein may beimplemented on computer hardware or a combination of computer softwareand hardware. For example, they may be implemented in an operatingsystem kernel, in a separate user process, in a library package boundinto network applications, on a specially constructed machine, on anapplication-specific integrated circuit (ASIC), or on a networkinterface card.

Software/hardware hybrid implementations of at least some of the aspectsdisclosed herein may be implemented on a programmable network-residentmachine (which should be understood to include intermittently connectednetwork-aware machines) selectively activated or reconfigured by acomputer program stored in memory. Such network devices may havemultiple network interfaces that may be configured or designed toutilize different types of network communication protocols. A generalarchitecture for some of these machines may be described herein in orderto illustrate one or more exemplary means by which a given unit offunctionality may be implemented. According to specific aspects, atleast some of the features or functionalities of the various aspectsdisclosed herein may be implemented on one or more general-purposecomputers associated with one or more networks, such as for example anend-user computer system, a client computer, a network server or otherserver system, a mobile computing device (e.g., tablet computing device,mobile phone, smartphone, laptop, or other appropriate computingdevice), a consumer electronic device, a music player, or any othersuitable electronic device, router, switch, or other suitable device, orany combination thereof. In at least some aspects, at least some of thefeatures or functionalities of the various aspects disclosed herein maybe implemented in one or more virtualized computing environments (e.g.,network computing clouds, virtual machines hosted on one or morephysical computing machines, or other appropriate virtual environments).

In other cases, purpose-built computing devices designed to performspecific functions may be used. In some cases, the purpose-builtcomputing devices may comprise application-specific integrated circuits.This is particularly the case where high speed or real time performanceis required, or where limitations are placed on physical size, memorycapacity, processor speed due to space constraints, budgetaryconstraints, and the like. Such purpose-built computing systems may beembedded into other devices (where they are often called “embeddedsystems”).

Referring now to FIG. 20 , there is shown a block diagram depicting anexemplary computing device 10 suitable for implementing at least aportion of the features or functionalities disclosed herein. Computingdevice 10 may be, for example, any one of the computing machines listedin the previous paragraph, or indeed any other electronic device capableof executing software- or hardware-based instructions according to oneor more programs stored in memory. Computing device 10 may be configuredto communicate with a plurality of other computing devices, such asclients or servers, over communications networks such as a wide areanetwork a metropolitan area network, a local area network, a wirelessnetwork, the Internet, or any other network, using known protocols forsuch communication, whether wireless or wired.

In one aspect, computing device 10 includes one or more centralprocessing units (CPU) 12, one or more interfaces 15, and one or morebusses 14 (such as a peripheral component interconnect (PCI) bus). Whenacting under the control of appropriate software or firmware, CPU 12 maybe responsible for implementing specific functions associated with thefunctions of a specifically configured computing device or machine. Forexample, in at least one aspect, a computing device 10 may be configuredor designed to function as a server system utilizing CPU 12, localmemory 11 and/or remote memory 16, and interface(s) 15. In at least oneaspect, CPU 12 may be caused to perform one or more of the differenttypes of functions and/or operations under the control of softwaremodules or components, which for example, may include an operatingsystem and any appropriate applications software, drivers, and the like.

CPU 12 may include one or more processors 13 such as, for example, aprocessor from one of the Intel, ARM, Qualcomm, and AMD families ofmicroprocessors. In some aspects, processors 13 may include speciallydesigned hardware such as application-specific integrated circuits(ASICs), electrically erasable programmable read-only memories(EEPROMs), field-programmable gate arrays (FPGAs), and so forth, forcontrolling operations of computing device 10. In a particular aspect, alocal memory 11 (such as non-volatile random access memory (RAM) and/orread-only memory (ROM), including for example one or more levels ofcached memory) may also form part of CPU 12. However, there are manydifferent ways in which memory may be coupled to system 10. Memory 11may be used for a variety of purposes such as, for example, cachingand/or storing data, programming instructions, and the like. It shouldbe further appreciated that CPU 12 may be one of a variety ofsystem-on-a-chip (SOC) type hardware that may include additionalhardware such as memory or graphics processing chips, such as a QUALCOMMSNAPDRAGON™ or SAMSUNG EXYNOS™ CPU as are becoming increasingly commonin the art, such as for use in mobile devices or integrated devices.

As used herein, the term “processor” is not limited merely to thoseintegrated circuits referred to in the art as a processor, a mobileprocessor, or a microprocessor, but broadly refers to a microcontroller,a microcomputer, a programmable logic controller, anapplication-specific integrated circuit, and any other programmablecircuit.

In one aspect, interfaces 15 are provided as network interface cards(NICs). Generally, NICs control the sending and receiving of datapackets over a computer network; other types of interfaces 15 may forexample support other peripherals used with computing device 10. Amongthe interfaces that may be provided are Ethernet interfaces, frame relayinterfaces, cable interfaces, DSL interfaces, token ring interfaces,graphics interfaces, and the like. In addition, various types ofinterfaces may be provided such as, for example, universal serial bus(USB), Serial, Ethernet, FIREWIRE™, THUNDERBOLT™, PCI, parallel, radiofrequency (radio frequency signal), BLUETOOTH™, near-fieldcommunications (e.g., using near-field magnetics), 802.11 (WiFi), framerelay, TCP/IP, ISDN, fast Ethernet interfaces, Gigabit Ethernetinterfaces, Serial ATA (SATA) or external SATA (ESATA) interfaces,high-definition multimedia interface (HDMI), digital visual interface(DVI), analog or digital audio interfaces, asynchronous transfer mode(ATM) interfaces, high-speed serial interface (HSSI) interfaces, Pointof Sale (POS) interfaces, fiber data distributed interfaces (FDDIs), andthe like. Generally, such interfaces 15 may include physical portsappropriate for communication with appropriate media. In some cases,they may also include an independent processor (such as a dedicatedaudio or video processor, as is common in the art for high-fidelity A/Vhardware interfaces) and, in some instances, volatile and/ornon-volatile memory (e.g., RAM).

Although the system shown in FIG. 20 illustrates one specificarchitecture for a computing device 10 for implementing one or more ofthe aspects described herein, it is by no means the only devicearchitecture on which at least a portion of the features and techniquesdescribed herein may be implemented. For example, architectures havingone or any number of processors 13 may be used, and such processors 13may be present in a single device or distributed among any number ofdevices. In one aspect, a single processor 13 handles communications aswell as routing computations, while in other aspects a separatededicated communications processor may be provided. In various aspects,different types of features or functionalities may be implemented in asystem according to the aspect that includes a client device (such as atablet device or smartphone running client software) and server systems(such as a server system described in more detail below).

Regardless of network device configuration, the system of an aspect mayemploy one or more memories or memory modules (such as, for example,remote memory block 16 and local memory 11) configured to store data,program instructions for the general-purpose network operations, orother information relating to the functionality of the aspects describedherein (or any combinations of the above). Program instructions maycontrol execution of or comprise an operating system and/or one or moreapplications, for example. Memory 16 or memories 11, 16 may also beconfigured to store data structures, configuration data, encryptiondata, historical system operations information, or any other specific orgeneric non-program information described herein.

Because such information and program instructions may be employed toimplement one or more systems or methods described herein, at least somenetwork device aspects may include nontransitory machine-readablestorage media, which, for example, may be configured or designed tostore program instructions, state information, and the like forperforming various operations described herein. Examples of suchnontransitory machine-readable storage media include, but are notlimited to, magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROM disks; magneto-optical mediasuch as optical disks, and hardware devices that are speciallyconfigured to store and perform program instructions, such as read-onlymemory devices (ROM), flash memory (as is common in mobile devices andintegrated systems), solid state drives (SSD) and “hybrid SSD” storagedrives that may combine physical components of solid state and hard diskdrives in a single hardware device (as are becoming increasingly commonin the art with regard to personal computers), memristor memory, randomaccess memory (RAM), and the like. It should be appreciated that suchstorage means may be integral and non-removable (such as RAM hardwaremodules that may be soldered onto a motherboard or otherwise integratedinto an electronic device), or they may be removable such as swappableflash memory modules (such as “thumb drives” or other removable mediadesigned for rapidly exchanging physical storage devices),“hot-swappable” hard disk drives or solid state drives, removableoptical storage discs, or other such removable media, and that suchintegral and removable storage media may be utilized interchangeably.Examples of program instructions include both object code, such as maybe produced by a compiler, machine code, such as may be produced by anassembler or a linker, byte code, such as may be generated by forexample a JAVA™ compiler and may be executed using a Java virtualmachine or equivalent, or files containing higher level code that may beexecuted by the computer using an interpreter (for example, scriptswritten in Python, Perl, Ruby, Groovy, or any other scripting language).

In some aspects, systems may be implemented on a standalone computingsystem. Referring now to FIG. 21 , there is shown a block diagramdepicting a typical exemplary architecture of one or more aspects orcomponents thereof on a standalone computing system. Computing device 20includes processors 21 that may run software that carry out one or morefunctions or applications of aspects, such as for example a clientapplication 24. Processors 21 may carry out computing instructions undercontrol of an operating system 22 such as, for example, a version ofMICROSOFT WINDOWS™ operating system, APPLE macOS™ or iOS™ operatingsystems, some variety of the Linux operating system, ANDROID™ operatingsystem, or the like. In many cases, one or more shared services 23 maybe operable in system 20, and may be useful for providing commonservices to client applications 24. Services 23 may for example beWINDOWS™ services, user-space common services in a Linux environment, orany other type of common service architecture used with operating system21. Input devices 28 may be of any type suitable for receiving userinput, including for example a keyboard, touchscreen, microphone (forexample, for voice input), mouse, touchpad, trackball, or anycombination thereof. Output devices 27 may be of any type suitable forproviding output to one or more users, whether remote or local to system20, and may include for example one or more screens for visual output,speakers, printers, or any combination thereof. Memory 25 may berandom-access memory having any structure and architecture known in theart, for use by processors 21, for example to run software. Storagedevices 26 may be any magnetic, optical, mechanical, mradio frequencysignalistor, or electrical storage device for storage of data in digitalform (such as those described above, referring to FIG. 20 ). Examples ofstorage devices 26 include flash memory, magnetic hard drive, CD-ROM,and/or the like.

In some aspects, systems may be implemented on a distributed computingnetwork, such as one having any number of clients and/or servers.Referring now to FIG. 22 , there is shown a block diagram depicting anexemplary architecture 30 for implementing at least a portion of asystem according to one aspect on a distributed computing network.According to the aspect, any number of clients 33 may be provided. Eachclient 33 may run software for implementing client-side portions of asystem; clients may comprise a system 20 such as that illustrated inFIG. 21 . In addition, any number of servers 32 may be provided forhandling requests received from one or more clients 33. Clients 33 andservers 32 may communicate with one another via one or more electronicnetworks 31, which may be in various aspects any of the Internet, a widearea network, a mobile telephony network (such as CDMA or GSM cellularnetworks), a wireless network (such as WiFi, WiMAX, LTE, and so forth),or a local area network (or indeed any network topology known in theart; the aspect does not prefer any one network topology over anyother). Networks 31 may be implemented using any known networkprotocols, including for example wired and/or wireless protocols.

In addition, in some aspects, servers 32 may call external services 37when needed to obtain additional information, or to refer to additionaldata concerning a particular call. Communications with external services37 may take place, for example, via one or more networks 31. In variousaspects, external services 37 may comprise web-enabled services orfunctionality related to or installed on the hardware device itself. Forexample, in one aspect where client applications 24 are implemented on asmartphone or other electronic device, client applications 24 may obtaininformation stored in a server system 32 in the cloud or on an externalservice 37 deployed on one or more of a particular enterprise's oruser's premises.

In some aspects, clients 33 or servers 32 (or both) may make use of oneor more specialized services or appliances that may be deployed locallyor remotely across one or more networks 31. For example, one or moredatabases 34 may be used or referred to by one or more aspects. Itshould be understood by one having ordinary skill in the art thatdatabases 34 may be arranged in a wide variety of architectures andusing a wide variety of data access and manipulation means. For example,in various aspects one or more databases 34 may comprise a relationaldatabase system using a structured query language (SQL), while othersmay comprise an alternative data storage technology such as thosereferred to in the art as “NoSQL” (for example, HADOOP CASSANDRA™,GOOGLE BIGTABLE™, and so forth). In some aspects, variant databasearchitectures such as column-oriented databases, in-memory databases,clustered databases, distributed databases, or even flat file datarepositories may be used according to the aspect. It will be appreciatedby one having ordinary skill in the art that any combination of known orfuture database technologies may be used as appropriate, unless aspecific database technology or a specific arrangement of components isspecified for a particular aspect described herein. Moreover, it shouldbe appreciated that the term “database” as used herein may refer to aphysical database machine, a cluster of machines acting as a singledatabase system, or a logical database within an overall databasemanagement system. Unless a specific meaning is specified for a givenuse of the term “database”, it should be construed to mean any of thesesenses of the word, all of which are understood as a plain meaning ofthe term “database” by those having ordinary skill in the art.

Similarly, some aspects may make use of one or more security systems 36and configuration systems 35. Security and configuration management arecommon information technology (IT) and web functions, and some amount ofeach are generally associated with any IT or web systems. It should beunderstood by one having ordinary skill in the art that anyconfiguration or security subsystems known in the art now or in thefuture may be used in conjunction with aspects without limitation,unless a specific security 36 or configuration system 35 or approach isspecifically required by the description of any specific aspect.

FIG. 23 shows an exemplary overview of a computer system 40 as may beused in any of the various locations throughout the system. It isexemplary of any computer that may execute code to process data. Variousmodifications and changes may be made to computer system 40 withoutdeparting from the broader scope of the system and method disclosedherein. Central processor unit (CPU) 41 is connected to bus 42, to whichbus is also connected memory 43, nonvolatile memory 44, display 47,input/output (I/O) unit 48, and network interface card (NIC) 53. I/Ounit 48 may, typically, be connected to keyboard 49, pointing device 50,hard disk 52, and real-time clock 51. NIC 53 connects to network 54,which may be the Internet or a local network, which local network may ormay not have connections to the Internet. Also shown as part of system40 is power supply unit 45 connected, in this example, to a mainalternating current (AC) supply 46. Not shown are batteries that couldbe present, and many other devices and modifications that are well knownbut are not applicable to the specific novel functions of the currentsystem and method disclosed herein. It should be appreciated that someor all components illustrated may be combined, such as in variousintegrated applications, for example Qualcomm or Samsungsystem-on-a-chip (SOC) devices, or whenever it may be appropriate tocombine multiple capabilities or functions into a single hardware device(for instance, in mobile devices such as smartphones, video gameconsoles, in-vehicle computer systems such as navigation or multimediasystems in automobiles, or other integrated hardware devices).

In various aspects, functionality for implementing systems or methods ofvarious aspects may be distributed among any number of client and/orserver components. For example, various software modules may beimplemented for performing various functions in connection with thesystem of any particular aspect, and such modules may be variouslyimplemented to run on server and/or client components.

The skilled person will be aware of a range of possible modifications ofthe various aspects described above. Accordingly, the present inventionis defined by the claims and their equivalents.

What is claimed is:
 1. A system for large-area, wide-bandwidth radiofrequency signal collection and processing, comprising: one or morecollection platforms, each comprising: one or more radio frequencysensor systems configured to capture signal information comprising radiofrequency data; and one or more metadata collectors configured tocapture signal information comprising metadata about the radio frequencydata, including at least a time, a frequency, and a location of capture;and an emission source analyzer comprising a processor, a memory, and aplurality of programming instructions stored in the memory which, whenoperating on the processor, cause the processor to: receive signalinformation from one or more of the collection platforms; identify oneor more radio frequency signals from an emission source; determine ascope of data collection for generation of radio frequency landscapeinformation for the emission source; and based on the determined scopeof data collection, direct one or more of the one or more collectionplatforms to collect more detailed data, less detailed data, wider areadata, narrower area data, higher resolution data, and/or lowerresolution data either by the same sensor at a different location ortime, or by at least one other sensor system on a related or unrelatedcollection platform or platforms; an enriched dataset generatorcomprising at least a plurality of programming instructions stored in amemory which, when operating on a processor, cause the processor to:receive a plurality of datasets comprising pre-processed signalinformation and further comprising at least one common or overlappingcharacteristic; separate each dataset into at least a spatial component,a time domain component, and a frequency domain component; map eachelement of each component of each dataset to their analogues in each ofthe other datasets in the plurality of datasets; develop statistics andmetrics for one or more of the components; and combine the plurality ofdatasets, their components, and the mapping between them into a singledataset of higher information content than any one of the datasetscomprising the plurality of datasets.
 2. The system of claim 1, whereinthe enriched dataset comprises sufficient information to produce ageographical map of radio frequency signals in a given area.
 3. Thesystem of claim 1, wherein the enriched dataset further comprisesderivative information allowing production of a geographical map ofradio frequency signals in a given area supplemented with analysis andstatistics of radio frequency activity.
 4. The system of claim 1,wherein the enriched dataset further comprises time information allowingproduction of a geographical map showing changes over time in the map ofradio frequency signals in a given area.
 5. The system of claim 4,wherein the enriched dataset further comprises population densityinformation allowing production of a geographical map showing changes inpopulation density over time relative to changes over time of radiofrequency signals in a given area.
 6. The system of claim 4, wherein theenriched dataset further comprises other non-radio-frequency-signalinformation allowing production of a map showing changes in the othernon-radio-frequency-signal information over time relative to changesover time of radio frequency signals in a given area.
 7. A method forlarge-area, wide-bandwidth radio frequency signal collection andprocessing, comprising the steps of: capturing signal informationcomprising radio frequency data and using radio frequency sensor systemson one or more collection platforms; capturing signal informationcomprising metadata about the radio frequency data, including at least atime, a frequency, and a location of capture, using metadata collectorson one or more collection platforms; and receiving signal information atan emission source analyzer from one or more of the collectionplatforms; identifying one or more radio frequency signals from anemission source; determining a scope of data collection for generationof radio frequency landscape information for the emission source; andbased on the determined scope of data collection, directing one or moreof the one or more collection platforms to collect more detailed data,less detailed data, wider area data, narrower area data, higherresolution data, and/or lower resolution data either by the same sensorat a different location or time, or by at least one other sensor systemon a related or unrelated collection platform or platforms; receivinginto an enriched dataset a plurality of datasets comprisingpre-processed signal information and further comprising at least onecommon or overlapping characteristic; separating each dataset into atleast a spatial component, a time domain component, and a frequencydomain component; mapping each element of each component of each datasetto their analogues in each of the other datasets in the plurality ofdatasets; developing statistics and metrics for one or more of thecomponents; and combining the plurality of datasets, their components,and the mapping between them into a single dataset of higher informationcontent than any one of the datasets comprising the plurality ofdatasets.
 8. The method of claim 7, wherein the enriched datasetcomprises sufficient information to produce a geographical map of radiofrequency signals in a given area.
 9. The method of claim 7, wherein theenriched dataset further comprises derivative information allowingproduction of a geographical map of radio frequency signals in a givenarea supplemented with analysis and statistics of radio frequencyactivity.
 10. The method of claim 7, wherein the enriched datasetfurther comprises time information allowing production of a geographicalmap showing changes over time in the map of radio frequency signals in agiven area.
 11. The method of claim 10, wherein the enriched datasetfurther comprises population density information allowing production ofa geographical map showing changes in population density over timerelative to changes over time of radio frequency signals in a givenarea.
 12. The method of claim 10, wherein the enriched dataset furthercomprises other non-radio-frequency-signal information allowingproduction of a map showing changes in the othernon-radio-frequency-signal information over time relative to changesover time of radio frequency signals in a given area.